class_docstr: Binary cross-entropy generator loss function.
  
  This callable applies the standard non-saturating binary cross-entropy
  loss for training GANs. It is a sign flip of the discriminator loss term
  and includes no 'fake' logit calculation.
  
  Args:
      logits: Logits of the input.
      target: The target for crossentropy. Typically, this will be a ones
          tensor of the same size as ``logits``.
  
  Returns:
      The binary cross-entropy generator loss.
code: def __call__(self, logits: Tensor, target: Tensor) -> Tensor:
      if target.ndim == 0:
          target = target * torch.ones_like(logits)
      elif target.ndim != logits.ndim:
          raise ValueError("Misconfigured GAN loss starget dimension.")
  
      return F.binary_cross_entropy_with_logits(logits, target)
  
file_path: NeuralCompression/neuralcompression/loss_fn/_binary_crossentropy_loss.py
imports: import torch
  import torch.nn.functional
  from torch import Tensor
  from ._gan_losses import DiscriminatorLoss, GeneratorLoss
label_desc: Use image node_id 1 for calling the BinaryCrossentropyGeneratorLoss obj's underlying member method code with example usage: obj.__call__(logits, target) and returns: F